Overview

Dataset statistics

Number of variables13
Number of observations9841
Missing cells0
Missing cells (%)0.0%
Duplicate rows2
Duplicate rows (%)< 0.1%
Total size in memory1.1 MiB
Average record size in memory112.0 B

Variable types

Text2
Numeric11

Alerts

Dataset has 2 (< 0.1%) duplicate rowsDuplicates
Murder is highly overall correlated with Rape and 3 other fieldsHigh correlation
Rape is highly overall correlated with Murder and 3 other fieldsHigh correlation
Kidnapping and Abduction is highly overall correlated with Murder and 1 other fieldsHigh correlation
Hurt is highly overall correlated with Murder and 2 other fieldsHigh correlation
Other Crimes Against SCs is highly overall correlated with Murder and 2 other fieldsHigh correlation
Murder is highly skewed (γ1 = 22.54124823)Skewed
Kidnapping and Abduction is highly skewed (γ1 = 27.35355613)Skewed
Robbery is highly skewed (γ1 = 25.31883621)Skewed
Arson is highly skewed (γ1 = 20.66903521)Skewed
Protection of Civil Rights (PCR) Act is highly skewed (γ1 = 27.1723461)Skewed
Murder has 6490 (65.9%) zerosZeros
Rape has 5157 (52.4%) zerosZeros
Kidnapping and Abduction has 7617 (77.4%) zerosZeros
Dacoity has 9434 (95.9%) zerosZeros
Robbery has 9083 (92.3%) zerosZeros
Arson has 8181 (83.1%) zerosZeros
Hurt has 5226 (53.1%) zerosZeros
Prevention of atrocities (POA) Act has 4247 (43.2%) zerosZeros
Protection of Civil Rights (PCR) Act has 9052 (92.0%) zerosZeros
Other Crimes Against SCs has 4360 (44.3%) zerosZeros

Reproduction

Analysis started2023-09-14 17:07:59.408984
Analysis finished2023-09-14 17:09:13.339811
Duration1 minute and 13.93 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Distinct70
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size153.8 KiB
2023-09-14T22:39:14.225324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length17
Median length13
Mean length9.8073367
Min length3

Characters and Unicode

Total characters96514
Distinct characters48
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowANDHRA PRADESH
2nd rowANDHRA PRADESH
3rd rowANDHRA PRADESH
4th rowANDHRA PRADESH
5th rowANDHRA PRADESH
ValueCountFrequency (%)
pradesh 2448
 
17.3%
uttar 955
 
6.8%
madhya 683
 
4.8%
maharashtra 598
 
4.2%
bihar 585
 
4.1%
tamil 509
 
3.6%
nadu 509
 
3.6%
rajasthan 498
 
3.5%
odisha 467
 
3.3%
453
 
3.2%
Other values (37) 6423
45.5%
2023-09-14T22:39:15.701323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 20217
20.9%
H 8947
 
9.3%
R 8637
 
8.9%
S 5465
 
5.7%
T 5325
 
5.5%
D 5184
 
5.4%
4287
 
4.4%
N 3875
 
4.0%
M 3824
 
4.0%
E 3480
 
3.6%
Other values (38) 27273
28.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 85233
88.3%
Lowercase Letter 6535
 
6.8%
Space Separator 4287
 
4.4%
Other Punctuation 459
 
0.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 20217
23.7%
H 8947
10.5%
R 8637
10.1%
S 5465
 
6.4%
T 5325
 
6.2%
D 5184
 
6.1%
N 3875
 
4.5%
M 3824
 
4.5%
E 3480
 
4.1%
U 3131
 
3.7%
Other values (13) 17148
20.1%
Lowercase Letter
ValueCountFrequency (%)
a 1737
26.6%
h 769
11.8%
r 736
11.3%
s 496
 
7.6%
d 431
 
6.6%
t 431
 
6.6%
e 322
 
4.9%
n 290
 
4.4%
i 266
 
4.1%
m 202
 
3.1%
Other values (13) 855
13.1%
Space Separator
ValueCountFrequency (%)
4287
100.0%
Other Punctuation
ValueCountFrequency (%)
& 459
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 91768
95.1%
Common 4746
 
4.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 20217
22.0%
H 8947
 
9.7%
R 8637
 
9.4%
S 5465
 
6.0%
T 5325
 
5.8%
D 5184
 
5.6%
N 3875
 
4.2%
M 3824
 
4.2%
E 3480
 
3.8%
U 3131
 
3.4%
Other values (36) 23683
25.8%
Common
ValueCountFrequency (%)
4287
90.3%
& 459
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 96514
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 20217
20.9%
H 8947
 
9.3%
R 8637
 
8.9%
S 5465
 
5.7%
T 5325
 
5.5%
D 5184
 
5.4%
4287
 
4.4%
N 3875
 
4.0%
M 3824
 
4.0%
E 3480
 
3.6%
Other values (38) 27273
28.3%
Distinct832
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Memory size153.8 KiB
2023-09-14T22:39:17.027328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length20
Median length17
Mean length8.372015
Min length3

Characters and Unicode

Total characters82389
Distinct characters38
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)0.4%

Sample

1st rowADILABAD
2nd rowANANTAPUR
3rd rowCHITTOOR
4th rowCUDDAPAH
5th rowEAST GODAVARI
ValueCountFrequency (%)
total 456
 
3.9%
rural 343
 
2.9%
commr 229
 
1.9%
rly 221
 
1.9%
west 120
 
1.0%
g.r.p 115
 
1.0%
east 111
 
0.9%
nagar 99
 
0.8%
south 91
 
0.8%
north 91
 
0.8%
Other values (759) 9921
84.1%
2023-09-14T22:39:19.080549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 15586
18.9%
R 8434
 
10.2%
I 4672
 
5.7%
N 4647
 
5.6%
H 4593
 
5.6%
U 4478
 
5.4%
L 3868
 
4.7%
T 3679
 
4.5%
O 3228
 
3.9%
D 3087
 
3.7%
Other values (28) 26117
31.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 79122
96.0%
Space Separator 1956
 
2.4%
Other Punctuation 1022
 
1.2%
Dash Punctuation 84
 
0.1%
Close Punctuation 56
 
0.1%
Open Punctuation 56
 
0.1%
Decimal Number 52
 
0.1%
Lowercase Letter 39
 
< 0.1%
Connector Punctuation 2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 15586
19.7%
R 8434
 
10.7%
I 4672
 
5.9%
N 4647
 
5.9%
H 4593
 
5.8%
U 4478
 
5.7%
L 3868
 
4.9%
T 3679
 
4.6%
O 3228
 
4.1%
D 3087
 
3.9%
Other values (15) 22850
28.9%
Other Punctuation
ValueCountFrequency (%)
. 1009
98.7%
/ 11
 
1.1%
& 2
 
0.2%
Lowercase Letter
ValueCountFrequency (%)
a 13
33.3%
n 13
33.3%
d 13
33.3%
Decimal Number
ValueCountFrequency (%)
2 26
50.0%
4 26
50.0%
Space Separator
ValueCountFrequency (%)
1956
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 84
100.0%
Close Punctuation
ValueCountFrequency (%)
) 56
100.0%
Open Punctuation
ValueCountFrequency (%)
( 56
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 79161
96.1%
Common 3228
 
3.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 15586
19.7%
R 8434
 
10.7%
I 4672
 
5.9%
N 4647
 
5.9%
H 4593
 
5.8%
U 4478
 
5.7%
L 3868
 
4.9%
T 3679
 
4.6%
O 3228
 
4.1%
D 3087
 
3.9%
Other values (18) 22889
28.9%
Common
ValueCountFrequency (%)
1956
60.6%
. 1009
31.3%
- 84
 
2.6%
) 56
 
1.7%
( 56
 
1.7%
2 26
 
0.8%
4 26
 
0.8%
/ 11
 
0.3%
_ 2
 
0.1%
& 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 82389
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 15586
18.9%
R 8434
 
10.2%
I 4672
 
5.7%
N 4647
 
5.6%
H 4593
 
5.6%
U 4478
 
5.4%
L 3868
 
4.7%
T 3679
 
4.5%
O 3228
 
3.9%
D 3087
 
3.7%
Other values (28) 26117
31.7%

Year
Real number (ℝ)

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007.1617
Minimum2001
Maximum2013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size153.8 KiB
2023-09-14T22:39:19.614548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2001
5-th percentile2001
Q12004
median2007
Q32010
95-th percentile2013
Maximum2013
Range12
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.7554538
Coefficient of variation (CV)0.0018710271
Kurtosis-1.2202554
Mean2007.1617
Median Absolute Deviation (MAD)3
Skewness-0.051953776
Sum19752478
Variance14.103433
MonotonicityIncreasing
2023-09-14T22:39:20.131552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2013 823
 
8.4%
2012 811
 
8.2%
2011 791
 
8.0%
2010 779
 
7.9%
2009 767
 
7.8%
2008 761
 
7.7%
2007 743
 
7.6%
2006 740
 
7.5%
2005 734
 
7.5%
2004 729
 
7.4%
Other values (3) 2163
22.0%
ValueCountFrequency (%)
2001 716
7.3%
2002 719
7.3%
2003 728
7.4%
2004 729
7.4%
2005 734
7.5%
2006 740
7.5%
2007 743
7.6%
2008 761
7.7%
2009 767
7.8%
2010 779
7.9%
ValueCountFrequency (%)
2013 823
8.4%
2012 811
8.2%
2011 791
8.0%
2010 779
7.9%
2009 767
7.8%
2008 761
7.7%
2007 743
7.6%
2006 740
7.5%
2005 734
7.5%
2004 729
7.4%

Murder
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct81
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7429123
Minimum0
Maximum423
Zeros6490
Zeros (%)65.9%
Negative0
Negative (%)0.0%
Memory size153.8 KiB
2023-09-14T22:39:20.768565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile6
Maximum423
Range423
Interquartile range (IQR)1

Descriptive statistics

Standard deviation11.88753
Coefficient of variation (CV)6.8204981
Kurtosis600.1811
Mean1.7429123
Median Absolute Deviation (MAD)0
Skewness22.541248
Sum17152
Variance141.31337
MonotonicityNot monotonic
2023-09-14T22:39:21.418547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6490
65.9%
1 1286
 
13.1%
2 737
 
7.5%
3 390
 
4.0%
4 243
 
2.5%
5 181
 
1.8%
6 106
 
1.1%
7 70
 
0.7%
8 57
 
0.6%
9 43
 
0.4%
Other values (71) 238
 
2.4%
ValueCountFrequency (%)
0 6490
65.9%
1 1286
 
13.1%
2 737
 
7.5%
3 390
 
4.0%
4 243
 
2.5%
5 181
 
1.8%
6 106
 
1.1%
7 70
 
0.7%
8 57
 
0.6%
9 43
 
0.4%
ValueCountFrequency (%)
423 1
< 0.1%
371 1
< 0.1%
323 1
< 0.1%
321 1
< 0.1%
318 1
< 0.1%
310 1
< 0.1%
288 1
< 0.1%
286 1
< 0.1%
239 1
< 0.1%
235 1
< 0.1%

Rape
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct118
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6564374
Minimum0
Maximum412
Zeros5157
Zeros (%)52.4%
Negative0
Negative (%)0.0%
Memory size153.8 KiB
2023-09-14T22:39:22.670547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile11
Maximum412
Range412
Interquartile range (IQR)3

Descriptive statistics

Standard deviation19.543118
Coefficient of variation (CV)5.3448525
Kurtosis243.7916
Mean3.6564374
Median Absolute Deviation (MAD)0
Skewness14.591131
Sum35983
Variance381.93348
MonotonicityNot monotonic
2023-09-14T22:39:23.305559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5157
52.4%
1 1231
 
12.5%
2 857
 
8.7%
3 610
 
6.2%
4 425
 
4.3%
5 329
 
3.3%
6 218
 
2.2%
7 185
 
1.9%
8 126
 
1.3%
9 113
 
1.1%
Other values (108) 590
 
6.0%
ValueCountFrequency (%)
0 5157
52.4%
1 1231
 
12.5%
2 857
 
8.7%
3 610
 
6.2%
4 425
 
4.3%
5 329
 
3.3%
6 218
 
2.2%
7 185
 
1.9%
8 126
 
1.3%
9 113
 
1.1%
ValueCountFrequency (%)
412 2
< 0.1%
397 2
< 0.1%
391 1
< 0.1%
375 1
< 0.1%
367 1
< 0.1%
357 1
< 0.1%
349 1
< 0.1%
343 1
< 0.1%
340 1
< 0.1%
335 2
< 0.1%

Kidnapping and Abduction
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct60
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0781425
Minimum0
Maximum363
Zeros7617
Zeros (%)77.4%
Negative0
Negative (%)0.0%
Memory size153.8 KiB
2023-09-14T22:39:23.953547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum363
Range363
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.3438241
Coefficient of variation (CV)7.7390738
Kurtosis910.75972
Mean1.0781425
Median Absolute Deviation (MAD)0
Skewness27.353556
Sum10610
Variance69.619401
MonotonicityNot monotonic
2023-09-14T22:39:24.616561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7617
77.4%
1 903
 
9.2%
2 465
 
4.7%
3 276
 
2.8%
4 148
 
1.5%
5 94
 
1.0%
6 79
 
0.8%
8 37
 
0.4%
7 32
 
0.3%
10 28
 
0.3%
Other values (50) 162
 
1.6%
ValueCountFrequency (%)
0 7617
77.4%
1 903
 
9.2%
2 465
 
4.7%
3 276
 
2.8%
4 148
 
1.5%
5 94
 
1.0%
6 79
 
0.8%
7 32
 
0.3%
8 37
 
0.4%
9 22
 
0.2%
ValueCountFrequency (%)
363 1
< 0.1%
304 1
< 0.1%
258 1
< 0.1%
254 1
< 0.1%
248 1
< 0.1%
219 2
< 0.1%
153 1
< 0.1%
130 1
< 0.1%
113 1
< 0.1%
99 1
< 0.1%

Dacoity
Real number (ℝ)

ZEROS 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.089421807
Minimum0
Maximum26
Zeros9434
Zeros (%)95.9%
Negative0
Negative (%)0.0%
Memory size153.8 KiB
2023-09-14T22:39:25.201564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum26
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.71676418
Coefficient of variation (CV)8.0155413
Kurtosis462.05469
Mean0.089421807
Median Absolute Deviation (MAD)0
Skewness18.202526
Sum880
Variance0.5137509
MonotonicityNot monotonic
2023-09-14T22:39:25.721547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 9434
95.9%
1 261
 
2.7%
2 61
 
0.6%
3 31
 
0.3%
4 17
 
0.2%
5 9
 
0.1%
7 7
 
0.1%
6 6
 
0.1%
8 3
 
< 0.1%
16 3
 
< 0.1%
Other values (6) 9
 
0.1%
ValueCountFrequency (%)
0 9434
95.9%
1 261
 
2.7%
2 61
 
0.6%
3 31
 
0.3%
4 17
 
0.2%
5 9
 
0.1%
6 6
 
0.1%
7 7
 
0.1%
8 3
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
26 1
 
< 0.1%
22 1
 
< 0.1%
20 1
 
< 0.1%
17 1
 
< 0.1%
16 3
< 0.1%
11 2
 
< 0.1%
9 3
< 0.1%
8 3
< 0.1%
7 7
0.1%
6 6
0.1%

Robbery
Real number (ℝ)

SKEWED  ZEROS 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.20627985
Minimum0
Maximum83
Zeros9083
Zeros (%)92.3%
Negative0
Negative (%)0.0%
Memory size153.8 KiB
2023-09-14T22:39:26.268548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum83
Range83
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.4666084
Coefficient of variation (CV)7.1097997
Kurtosis1132.2127
Mean0.20627985
Median Absolute Deviation (MAD)0
Skewness25.318836
Sum2030
Variance2.1509402
MonotonicityNot monotonic
2023-09-14T22:39:26.787562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 9083
92.3%
1 439
 
4.5%
2 142
 
1.4%
3 62
 
0.6%
4 24
 
0.2%
6 16
 
0.2%
8 11
 
0.1%
5 10
 
0.1%
7 9
 
0.1%
11 6
 
0.1%
Other values (14) 39
 
0.4%
ValueCountFrequency (%)
0 9083
92.3%
1 439
 
4.5%
2 142
 
1.4%
3 62
 
0.6%
4 24
 
0.2%
5 10
 
0.1%
6 16
 
0.2%
7 9
 
0.1%
8 11
 
0.1%
9 5
 
0.1%
ValueCountFrequency (%)
83 1
 
< 0.1%
37 1
 
< 0.1%
24 2
< 0.1%
22 2
< 0.1%
20 2
< 0.1%
19 3
< 0.1%
17 4
< 0.1%
16 2
< 0.1%
15 2
< 0.1%
14 4
< 0.1%

Arson
Real number (ℝ)

SKEWED  ZEROS 

Distinct50
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.59059039
Minimum0
Maximum178
Zeros8181
Zeros (%)83.1%
Negative0
Negative (%)0.0%
Memory size153.8 KiB
2023-09-14T22:39:27.395547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum178
Range178
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.6970309
Coefficient of variation (CV)6.2598901
Kurtosis693.07873
Mean0.59059039
Median Absolute Deviation (MAD)0
Skewness20.669035
Sum5812
Variance13.668037
MonotonicityNot monotonic
2023-09-14T22:39:28.046561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8181
83.1%
1 865
 
8.8%
2 330
 
3.4%
3 173
 
1.8%
4 78
 
0.8%
5 52
 
0.5%
7 24
 
0.2%
6 21
 
0.2%
10 12
 
0.1%
8 12
 
0.1%
Other values (40) 93
 
0.9%
ValueCountFrequency (%)
0 8181
83.1%
1 865
 
8.8%
2 330
 
3.4%
3 173
 
1.8%
4 78
 
0.8%
5 52
 
0.5%
6 21
 
0.2%
7 24
 
0.2%
8 12
 
0.1%
9 8
 
0.1%
ValueCountFrequency (%)
178 1
< 0.1%
103 1
< 0.1%
76 1
< 0.1%
66 1
< 0.1%
62 1
< 0.1%
61 1
< 0.1%
57 1
< 0.1%
53 2
< 0.1%
51 2
< 0.1%
50 1
< 0.1%

Hurt
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct245
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.985875
Minimum0
Maximum1252
Zeros5226
Zeros (%)53.1%
Negative0
Negative (%)0.0%
Memory size153.8 KiB
2023-09-14T22:39:28.731547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile39
Maximum1252
Range1252
Interquartile range (IQR)5

Descriptive statistics

Standard deviation52.538202
Coefficient of variation (CV)4.782341
Kurtosis151.06884
Mean10.985875
Median Absolute Deviation (MAD)0
Skewness11.046343
Sum108112
Variance2760.2627
MonotonicityNot monotonic
2023-09-14T22:39:29.421563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5226
53.1%
1 730
 
7.4%
2 572
 
5.8%
3 366
 
3.7%
4 295
 
3.0%
5 238
 
2.4%
7 197
 
2.0%
6 192
 
2.0%
8 160
 
1.6%
10 139
 
1.4%
Other values (235) 1726
 
17.5%
ValueCountFrequency (%)
0 5226
53.1%
1 730
 
7.4%
2 572
 
5.8%
3 366
 
3.7%
4 295
 
3.0%
5 238
 
2.4%
6 192
 
2.0%
7 197
 
2.0%
8 160
 
1.6%
9 134
 
1.4%
ValueCountFrequency (%)
1252 1
< 0.1%
950 1
< 0.1%
900 1
< 0.1%
890 1
< 0.1%
877 1
< 0.1%
858 1
< 0.1%
821 1
< 0.1%
817 1
< 0.1%
815 1
< 0.1%
722 1
< 0.1%

Prevention of atrocities (POA) Act
Real number (ℝ)

ZEROS 

Distinct326
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.155472
Minimum0
Maximum5584
Zeros4247
Zeros (%)43.2%
Negative0
Negative (%)0.0%
Memory size153.8 KiB
2023-09-14T22:39:30.076547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q315
95-th percentile87
Maximum5584
Range5584
Interquartile range (IQR)15

Descriptive statistics

Standard deviation160.20847
Coefficient of variation (CV)5.6901362
Kurtosis397.31576
Mean28.155472
Median Absolute Deviation (MAD)2
Skewness17.076394
Sum277078
Variance25666.754
MonotonicityNot monotonic
2023-09-14T22:39:30.678565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4247
43.2%
1 596
 
6.1%
2 477
 
4.8%
3 326
 
3.3%
4 263
 
2.7%
5 241
 
2.4%
6 186
 
1.9%
7 177
 
1.8%
8 165
 
1.7%
9 127
 
1.3%
Other values (316) 3036
30.9%
ValueCountFrequency (%)
0 4247
43.2%
1 596
 
6.1%
2 477
 
4.8%
3 326
 
3.3%
4 263
 
2.7%
5 241
 
2.4%
6 186
 
1.9%
7 177
 
1.8%
8 165
 
1.7%
9 127
 
1.3%
ValueCountFrequency (%)
5584 1
< 0.1%
4885 1
< 0.1%
4436 1
< 0.1%
3072 1
< 0.1%
3024 1
< 0.1%
2974 1
< 0.1%
2965 1
< 0.1%
2554 1
< 0.1%
2548 1
< 0.1%
2534 1
< 0.1%

Protection of Civil Rights (PCR) Act
Real number (ℝ)

SKEWED  ZEROS 

Distinct75
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.88039833
Minimum0
Maximum459
Zeros9052
Zeros (%)92.0%
Negative0
Negative (%)0.0%
Memory size153.8 KiB
2023-09-14T22:39:31.325561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum459
Range459
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.5536104
Coefficient of variation (CV)9.715614
Kurtosis1112.4613
Mean0.88039833
Median Absolute Deviation (MAD)0
Skewness27.172346
Sum8664
Variance73.164251
MonotonicityNot monotonic
2023-09-14T22:39:31.976547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9052
92.0%
1 279
 
2.8%
2 118
 
1.2%
3 66
 
0.7%
4 41
 
0.4%
5 32
 
0.3%
6 23
 
0.2%
12 17
 
0.2%
8 13
 
0.1%
7 13
 
0.1%
Other values (65) 187
 
1.9%
ValueCountFrequency (%)
0 9052
92.0%
1 279
 
2.8%
2 118
 
1.2%
3 66
 
0.7%
4 41
 
0.4%
5 32
 
0.3%
6 23
 
0.2%
7 13
 
0.1%
8 13
 
0.1%
9 11
 
0.1%
ValueCountFrequency (%)
459 1
< 0.1%
312 1
< 0.1%
198 1
< 0.1%
165 1
< 0.1%
153 1
< 0.1%
149 1
< 0.1%
133 1
< 0.1%
123 1
< 0.1%
122 2
< 0.1%
113 1
< 0.1%

Other Crimes Against SCs
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct391
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.868001
Minimum0
Maximum5339
Zeros4360
Zeros (%)44.3%
Negative0
Negative (%)0.0%
Memory size153.8 KiB
2023-09-14T22:39:32.612562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q320
95-th percentile115
Maximum5339
Range5339
Interquartile range (IQR)20

Descriptive statistics

Standard deviation209.89602
Coefficient of variation (CV)5.8519019
Kurtosis267.37636
Mean35.868001
Median Absolute Deviation (MAD)2
Skewness15.162417
Sum352977
Variance44056.34
MonotonicityNot monotonic
2023-09-14T22:39:33.279561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4360
44.3%
1 438
 
4.5%
2 324
 
3.3%
3 256
 
2.6%
4 235
 
2.4%
5 210
 
2.1%
7 174
 
1.8%
6 169
 
1.7%
8 152
 
1.5%
10 135
 
1.4%
Other values (381) 3388
34.4%
ValueCountFrequency (%)
0 4360
44.3%
1 438
 
4.5%
2 324
 
3.3%
3 256
 
2.6%
4 235
 
2.4%
5 210
 
2.1%
6 169
 
1.7%
7 174
 
1.8%
8 152
 
1.5%
9 125
 
1.3%
ValueCountFrequency (%)
5339 1
< 0.1%
4771 1
< 0.1%
4536 1
< 0.1%
4296 1
< 0.1%
4239 1
< 0.1%
4014 1
< 0.1%
3978 1
< 0.1%
3974 1
< 0.1%
3795 1
< 0.1%
3683 1
< 0.1%

Interactions

2023-09-14T22:39:05.027397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:04.094764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:10.740407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:16.505418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:22.409403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:29.093412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:34.875412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:40.579405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:46.618414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:52.960403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:59.052397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:39:05.567403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:05.098395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:11.277404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:17.020403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:22.981413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:29.614404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:35.385404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:41.147406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:47.141423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:53.514404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:59.577403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:39:06.089404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:05.600403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:11.798402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:17.508421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:23.501406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:30.116409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:35.876403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:41.662403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:47.658411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:54.068409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:39:00.095422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:39:06.600419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:06.108402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:12.283422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:17.986403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:24.047412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:30.610409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:36.366421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:42.175406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:48.152405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:54.591422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:39:00.679425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:39:07.212413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:06.680404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:12.833426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:18.550405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:24.653424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:31.169404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:36.976404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:42.770406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:48.743412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:55.189399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:39:01.262407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:39:07.729406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:07.508402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:13.344403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:19.307403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:25.228409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:31.686405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:37.481405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:43.314403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:49.751407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:55.728404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:39:01.800403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:39:08.249406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:08.008420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:13.831401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:19.792413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:25.744422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:32.176406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:37.955408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:43.826405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:50.262395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:56.258419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:39:02.319407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:39:08.814403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:08.585410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:14.406404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:20.333421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:26.709405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:32.727406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:38.498404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:44.418418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:50.826394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:56.843397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:39:02.872404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:39:09.364404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:09.117408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:14.954402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:20.858404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:27.277402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:33.279403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:39.032412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:44.973408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:51.369413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:57.388402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:39:03.410403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:39:09.939401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:09.683425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:15.480404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:21.388403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:27.843401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:33.816410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:39.563402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:45.530425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:51.893404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:57.926404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:39:03.958404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:39:10.469402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:10.209406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:15.975405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:21.890418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:28.520412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:34.339418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:40.057421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:46.064404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:52.413409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:38:58.468421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-14T22:39:04.477403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-09-14T22:39:33.885804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
YearMurderRapeKidnapping and AbductionDacoityRobberyArsonHurtPrevention of atrocities (POA) ActProtection of Civil Rights (PCR) ActOther Crimes Against SCs
Year1.000-0.0010.0470.0550.003-0.046-0.051-0.014-0.038-0.1370.010
Murder-0.0011.0000.6240.5030.1810.2750.4420.5300.3040.1350.575
Rape0.0470.6241.0000.5120.1750.2610.4290.6120.2870.1230.683
Kidnapping and Abduction0.0550.5030.5121.0000.2270.3260.3450.4600.1950.0860.480
Dacoity0.0030.1810.1750.2271.0000.3410.2030.2000.1540.1450.195
Robbery-0.0460.2750.2610.3260.3411.0000.3010.3170.1780.1150.267
Arson-0.0510.4420.4290.3450.2030.3011.0000.4450.2400.0990.419
Hurt-0.0140.5300.6120.4600.2000.3170.4451.0000.3660.1230.619
Prevention of atrocities (POA) Act-0.0380.3040.2870.1950.1540.1780.2400.3661.0000.2160.296
Protection of Civil Rights (PCR) Act-0.1370.1350.1230.0860.1450.1150.0990.1230.2161.0000.149
Other Crimes Against SCs0.0100.5750.6830.4800.1950.2670.4190.6190.2960.1491.000

Missing values

2023-09-14T22:39:11.327411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-14T22:39:12.580257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

STATE/UTDISTRICTYearMurderRapeKidnapping and AbductionDacoityRobberyArsonHurtPrevention of atrocities (POA) ActProtection of Civil Rights (PCR) ActOther Crimes Against SCs
0ANDHRA PRADESHADILABAD2001014000301532
1ANDHRA PRADESHANANTAPUR20010400004921053
2ANDHRA PRADESHCHITTOOR20013300003836034
3ANDHRA PRADESHCUDDAPAH20010300002052025
4ANDHRA PRADESHEAST GODAVARI2001130000312637
5ANDHRA PRADESHGUNTAKAL RLY.20010000000000
6ANDHRA PRADESHGUNTUR20014510035316653
7ANDHRA PRADESHHYDERABAD CITY2001021000040135
8ANDHRA PRADESHKARIMNAGAR20018133105272610
9ANDHRA PRADESHKHAMMAM20012600001251090
STATE/UTDISTRICTYearMurderRapeKidnapping and AbductionDacoityRobberyArsonHurtPrevention of atrocities (POA) ActProtection of Civil Rights (PCR) ActOther Crimes Against SCs
813Delhi UTSOUTH-EAST20130000000314
814Delhi UTSOUTH-WEST20130000000404
815Delhi UTSTF20130000000000
816Delhi UTWEST20130000000310
817Delhi UTTOTAL2013000000026326
818LakshadweepLAKSHADWEEP20130000000000
819LakshadweepTOTAL20130000000000
820PuducherryKARAIKAL20130100000030
821PuducherryPUDUCHERRY201300000004121
822PuducherryTOTAL201301000004151

Duplicate rows

Most frequently occurring

STATE/UTDISTRICTYearMurderRapeKidnapping and AbductionDacoityRobberyArsonHurtPrevention of atrocities (POA) ActProtection of Civil Rights (PCR) ActOther Crimes Against SCs# duplicates
0JAMMU & KASHMIRANANTNAG200100000000002
1NAGALANDTOTAL200500000000002